Adaptive M-SVM classification model qualified indoor scene images with hybrid feature selection approach

@inproceedings{Singh2018AdaptiveMC,
  title={Adaptive M-SVM classification model qualified indoor scene images with hybrid feature selection approach},
  author={Gagandeep Singh and Shaheed Bhagat Singh},
  year={2018}
}
In this thesis, the multi-category dataset has been incorporated with the robust feature descriptor using the scale invariant feature transform (SIFT), SURF and FREAK along with the multi-category enabled support vector machine (mSVM). The multi-category support vector machine (mSVM) has been designed with the iterative phases to make it able to work with the multi-category dataset. The mSVM represents the training samples of main class as the primary class in every iterative phase and all… CONTINUE READING

References

Publications referenced by this paper.
SHOWING 1-10 OF 11 REFERENCES

Indoor scene recognition through object detection

  • 2010 IEEE International Conference on Robotics and Automation
  • 2010
VIEW 8 EXCERPTS
HIGHLY INFLUENTIAL

A database and challenge for acoustic scene classification and event detection

  • 21st European Signal Processing Conference (EUSIPCO 2013)
  • 2013
VIEW 2 EXCERPTS

A relational kernel-based approach to scene classification

  • 2013 IEEE Workshop on Applications of Computer Vision (WACV)
  • 2013
VIEW 1 EXCERPT

Blocks That Shout: Distinctive Parts for Scene Classification

  • 2013 IEEE Conference on Computer Vision and Pattern Recognition
  • 2013
VIEW 1 EXCERPT

Perceptual Organization and Recognition of Indoor Scenes from RGB-D Images

  • 2013 IEEE Conference on Computer Vision and Pattern Recognition
  • 2013
VIEW 2 EXCERPTS

Similar Papers

Loading similar papers…